def testPca(self, nTerms = 3):
tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(bgValue = self.bgValue)
tWcs = self.makeWcs(offset = 0)
sWcs = self.makeWcs(offset = 0)
tExp = afwImage.ExposureF(tMi, tWcs)
sExp = afwImage.ExposureF(sMi, sWcs)
sExp.setPsf(self.psf)
self.subconfigDF.usePcaForSpatialKernel = True
self.subconfigDF.numPrincipalComponents = nTerms
psfMatchDF = ipDiffim.ImagePsfMatchTask(config=self.configDF)
candList = psfMatchDF.makeCandidateList(tExp, sExp, self.ksize)
resultsDF = psfMatchDF.subtractMaskedImages(tMi, sMi, candList)
spatialKernel = resultsDF.psfMatchingKernel
spatialKernelSolution = spatialKernel.getSpatialParameters()
self.assertEqual(len(spatialKernelSolution), nTerms)
# First basis has no spatial variation
for i in range(1, nTerms):
self.assertEqual(spatialKernelSolution[0][i], 0.)
# All bases have correct number of terms
sko = self.subconfigDF.spatialKernelOrder
nSpatialTerms = int(0.5 * (sko + 1) * (sko + 2))
for i in range(len(spatialKernelSolution)):
self.assertEqual(len(spatialKernelSolution[i]), nSpatialTerms)
spatialBg = resultsDF.backgroundModel
spatialBgSolution = spatialBg.getParameters()
bgo = self.subconfigDF.spatialBgOrder
nBgTerms = int(0.5 * (bgo + 1) * (bgo + 2))
self.assertEqual(len(spatialBgSolution), nBgTerms)
def testSubtractMaskedImages(self):
# Lets do some additional testing here to make sure we recover
# the known spatial model. No background, just the faked
# alard-lupton basis set. The rest of matchMaskedImages() and
# subtractMaskedImages() functionality is tested by the
# Exposure-based methods.
fakeCoeffs = diffimTools.fakeCoeffs()
# Quick note; you shouldn't change confake here, since the
# candidates in the KernelCellSet are initialized in
# makeFakeKernelSet
tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(bgValue = 0.0, addNoise = False)
svar = sMi.getVariance()
svar.set(1.0)
tvar = tMi.getVariance()
tvar.set(1.0)
basisList = ipDiffim.makeKernelBasisList(confake.kernel.active)
psfMatchAL = ipDiffim.ImagePsfMatchTask(config=confake)
spatialSolution, psfMatchingKernel, backgroundModel = psfMatchAL._solve(kcs, basisList)
fitCoeffs = psfMatchingKernel.getSpatialParameters()
for b in range(len(fakeCoeffs)):
for s in range(len(fakeCoeffs[b])):
if fakeCoeffs[b][s] == 0.0:
self.assertAlmostEqual(fitCoeffs[b][s], 0.0)
else:
# OUTSTANDING ISSUE - WHY IS THIS ONE TERM OFF!?!?
if b != 4 and s != 0:
self.assertAlmostEqual(fitCoeffs[b][s]/fakeCoeffs[b][s], 1.0, 1)
def testSubtractExposures(self):
# Test all 3 options
tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(bgValue = self.bgValue)
tWcs = self.makeWcs(offset = 0)
sWcs = self.makeWcs(offset = 1)
tExp = afwImage.ExposureF(tMi, tWcs)
sExp = afwImage.ExposureF(sMi, sWcs)
sExp.setPsf(self.psf)
psfMatchAL = ipDiffim.ImagePsfMatchTask(config=self.configAL)
psfMatchDF = ipDiffim.ImagePsfMatchTask(config=self.configDF)
psfMatchDFr = ipDiffim.ImagePsfMatchTask(config=self.configDFr)
self.assertEqual(psfMatchAL.useRegularization, False)
self.assertEqual(psfMatchDF.useRegularization, False)
self.assertEqual(psfMatchDFr.useRegularization, True)
resultsAL = psfMatchAL.subtractExposures(tExp, sExp, doWarping = True)
psfMatchDF.subtractExposures(tExp, sExp, doWarping = True)
psfMatchDFr.subtractExposures(tExp, sExp, doWarping = True)
self.assertEqual(type(resultsAL.subtractedExposure), afwImage.ExposureF)
self.assertEqual(type(resultsAL.psfMatchingKernel), afwMath.LinearCombinationKernel)
self.assertEqual(type(resultsAL.backgroundModel), afwMath.Function2D)
self.assertEqual(type(resultsAL.kernelCellSet), afwMath.SpatialCellSet)
def testSubtractExposures(self):
# Test all 3 options
tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(
bgValue=self.bgValue)
tWcs = self.makeWcs(offset=0)
sWcs = self.makeWcs(offset=1)
tExp = afwImage.ExposureF(tMi, tWcs)
sExp = afwImage.ExposureF(sMi, sWcs)
sExp.setPsf(self.psf)
tExp.setPsf(self.psf)
psfMatchAL = ipDiffim.ImagePsfMatchTask(config=self.configAL)
psfMatchDF = ipDiffim.ImagePsfMatchTask(config=self.configDF)
psfMatchDFr = ipDiffim.ImagePsfMatchTask(config=self.configDFr)
self.assertEqual(psfMatchAL.useRegularization, False)
self.assertEqual(psfMatchDF.useRegularization, False)
self.assertEqual(psfMatchDFr.useRegularization, True)
resultsAL = psfMatchAL.subtractExposures(tExp, sExp, doWarping=True)
psfMatchDF.subtractExposures(tExp, sExp, doWarping=True)
psfMatchDFr.subtractExposures(tExp, sExp, doWarping=True)
self.assertEqual(type(resultsAL.subtractedExposure),
afwImage.ExposureF)
self.assertEqual(type(resultsAL.psfMatchingKernel),
afwMath.LinearCombinationKernel)
self.assertEqual(type(resultsAL.backgroundModel),
afwMath.Chebyshev1Function2D)
self.assertEqual(type(resultsAL.kernelCellSet), afwMath.SpatialCellSet)
def generateFakeImages():
tSigma = 1.5
tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(tGaussianWidth=tSigma, bgValue=200)
sSigma = 2.5
tWcs = generateFakeWcs()
sWcs = generateFakeWcs()
tExp = afwImage.ExposureF(tMi, tWcs)
sExp = afwImage.ExposureF(sMi, sWcs)
tPsf = measAlg.DoubleGaussianPsf(21, 21, tSigma)
sPsf = measAlg.DoubleGaussianPsf(21, 21, sSigma)
tExp.setPsf(tPsf)
sExp.setPsf(sPsf)
return tExp, sExp
def generateFakeImages():
tSigma = 1.5
tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(tGaussianWidth=tSigma, bgValue=200)
sSigma = 2.5
tWcs = generateFakeWcs()
sWcs = generateFakeWcs()
tExp = afwImage.ExposureF(tMi, tWcs)
sExp = afwImage.ExposureF(sMi, sWcs)
tPsf = measAlg.DoubleGaussianPsf(21, 21, tSigma)
sPsf = measAlg.DoubleGaussianPsf(21, 21, sSigma)
tExp.setPsf(tPsf)
sExp.setPsf(sPsf)
return tExp, sExp
def testSnap(self):
tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(bgValue = self.bgValue)
tWcs = self.makeWcs(offset = 0)
sWcs = self.makeWcs(offset = 0)
tExp = afwImage.ExposureF(tMi, tWcs)
sExp = afwImage.ExposureF(sMi, sWcs)
sExp.setPsf(self.psf)
psfMatchAL = ipDiffim.SnapPsfMatchTask(config=self.configAL)
psfMatchDF = ipDiffim.SnapPsfMatchTask(config=self.configDF)
psfMatchDFr = ipDiffim.SnapPsfMatchTask(config=self.configDFr)
psfMatchAL.subtractMaskedImages(tMi, sMi, psfMatchAL.makeCandidateList(tExp, sExp, self.ksize))
psfMatchDF.subtractMaskedImages(tMi, sMi, psfMatchDF.makeCandidateList(tExp, sExp, self.ksize))
psfMatchDFr.subtractMaskedImages(tMi, sMi, psfMatchDFr.makeCandidateList(tExp, sExp, self.ksize))
def testMatchExposures(self):
# Only test 1 option
tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(bgValue = self.bgValue)
tWcs = self.makeWcs(offset = 0)
sWcs = self.makeWcs(offset = 1)
tExp = afwImage.ExposureF(tMi, tWcs)
sExp = afwImage.ExposureF(sMi, sWcs)
sExp.setPsf(self.psf)
psfMatchAL = ipDiffim.ImagePsfMatchTask(config=self.configAL)
resultsAL = psfMatchAL.matchExposures(tExp, sExp,
templateFwhmPix = 2.0, scienceFwhmPix = 3.0, doWarping = True)
self.assertEqual(type(resultsAL.matchedExposure), afwImage.ExposureF)
self.assertEqual(type(resultsAL.psfMatchingKernel), afwMath.LinearCombinationKernel)
self.assertEqual(type(resultsAL.backgroundModel), afwMath.Function2D)
self.assertEqual(type(resultsAL.kernelCellSet), afwMath.SpatialCellSet)
def testWarping(self):
tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(bgValue = self.bgValue)
tWcs = self.makeWcs(offset = 0)
sWcs = self.makeWcs(offset = 1)
tExp = afwImage.ExposureF(tMi, tWcs)
sExp = afwImage.ExposureF(sMi, sWcs)
# Should fail due to registration problem
psfMatchAL = ipDiffim.ImagePsfMatchTask(config=self.configAL)
try:
psfMatchAL.subtractExposures(tExp, sExp, doWarping = True)
except Exception as e:
print "testWarning failed with %r" % (e,)
pass
else:
self.fail()
def testWarping(self):
tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(
bgValue=self.bgValue)
tWcs = self.makeWcs(offset=0)
sWcs = self.makeWcs(offset=1)
tExp = afwImage.ExposureF(tMi, tWcs)
sExp = afwImage.ExposureF(sMi, sWcs)
# Should fail due to registration problem
psfMatchAL = ipDiffim.ImagePsfMatchTask(config=self.configAL)
try:
psfMatchAL.subtractExposures(tExp, sExp, doWarping=True)
except Exception as e:
print("testWarning failed with %r" % (e, ))
pass
else:
self.fail()
def testMatchExposures(self):
# Only test 1 option
tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(
bgValue=self.bgValue)
tWcs = self.makeWcs(offset=0)
sWcs = self.makeWcs(offset=1)
tExp = afwImage.ExposureF(tMi, tWcs)
sExp = afwImage.ExposureF(sMi, sWcs)
sExp.setPsf(self.psf)
psfMatchAL = ipDiffim.ImagePsfMatchTask(config=self.configAL)
resultsAL = psfMatchAL.matchExposures(tExp,
sExp,
templateFwhmPix=2.0,
scienceFwhmPix=3.0,
doWarping=True)
self.assertEqual(type(resultsAL.matchedExposure), afwImage.ExposureF)
self.assertEqual(type(resultsAL.psfMatchingKernel),
afwMath.LinearCombinationKernel)
self.assertEqual(type(resultsAL.backgroundModel), afwMath.Function2D)
self.assertEqual(type(resultsAL.kernelCellSet), afwMath.SpatialCellSet)
def testSubtractMaskedImages(self):
# Lets do some additional testing here to make sure we recover
# the known spatial model. No background, just the faked
# alard-lupton basis set. The rest of matchMaskedImages() and
# subtractMaskedImages() functionality is tested by the
# Exposure-based methods.
fakeCoeffs = diffimTools.fakeCoeffs()
# Quick note; you shouldn't change confake here, since the
# candidates in the KernelCellSet are initialized in
# makeFakeKernelSet
tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(
bgValue=0.0, addNoise=False)
svar = sMi.getVariance()
svar.set(1.0)
tvar = tMi.getVariance()
tvar.set(1.0)
basisList = ipDiffim.makeKernelBasisList(confake.kernel.active)
psfMatchAL = ipDiffim.ImagePsfMatchTask(config=confake)
spatialSolution, psfMatchingKernel, backgroundModel = psfMatchAL._solve(
kcs, basisList)
fitCoeffs = psfMatchingKernel.getSpatialParameters()
for b in range(len(fakeCoeffs)):
for s in range(len(fakeCoeffs[b])):
if fakeCoeffs[b][s] == 0.0:
self.assertAlmostEqual(fitCoeffs[b][s], 0.0)
else:
# OUTSTANDING ISSUE - WHY IS THIS ONE TERM OFF!?!?
if b != 4 and s != 0:
self.assertAlmostEqual(
fitCoeffs[b][s] / fakeCoeffs[b][s], 1.0, 1)
def testPca(self, nTerms=3):
tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(
bgValue=self.bgValue)
tWcs = self.makeWcs(offset=0)
sWcs = self.makeWcs(offset=0)
tExp = afwImage.ExposureF(tMi, tWcs)
sExp = afwImage.ExposureF(sMi, sWcs)
sExp.setPsf(self.psf)
self.subconfigDF.usePcaForSpatialKernel = True
self.subconfigDF.numPrincipalComponents = nTerms
psfMatchDF = ipDiffim.ImagePsfMatchTask(config=self.configDF)
candList = psfMatchDF.makeCandidateList(tExp, sExp, self.ksize)
resultsDF = psfMatchDF.subtractMaskedImages(tMi, sMi, candList)
spatialKernel = resultsDF.psfMatchingKernel
spatialKernelSolution = spatialKernel.getSpatialParameters()
self.assertEqual(len(spatialKernelSolution), nTerms)
# First basis has no spatial variation
for i in range(1, nTerms):
self.assertEqual(spatialKernelSolution[0][i], 0.)
# All bases have correct number of terms
sko = self.subconfigDF.spatialKernelOrder
nSpatialTerms = int(0.5 * (sko + 1) * (sko + 2))
for i in range(len(spatialKernelSolution)):
self.assertEqual(len(spatialKernelSolution[i]), nSpatialTerms)
spatialBg = resultsDF.backgroundModel
spatialBgSolution = spatialBg.getParameters()
bgo = self.subconfigDF.spatialBgOrder
nBgTerms = int(0.5 * (bgo + 1) * (bgo + 2))
self.assertEqual(len(spatialBgSolution), nBgTerms)
